Root cause investigation of site speed performance anomalies

Abstract

The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Description

RELATED APPLICATIONS[0001]The subject matter of this application is related to the subject matter in a co-pending non-provisional application inventors Ritesh Maheshwari, Liang Zhang, Yang Yang, Jieying Chen, Ruixuan Hou, Steve S. Noble, David Q. He, Sanjay S. Dubey and Deepak Agarwal, entitled “Statistical Detection of Site Speed Performance Anomalies,” having Ser. No. 14 / 956,095, and filing date Dec. 1, 2015.[0002]The subject matter of this application is also related to the subject matter in a co-pending non-provisional application by inventors Ritesh Maheshwari, Liang Zhang, Yang Yang, Jieying Chen, Toon Sripatanaskul, Ruixuan Hou, Steve S. Noble, David Q. He, Sanjay S. Dubey and Deepak Agarwal, entitled “Analysis of Site Speed Performance Anomalies Caused by Server-Side Issues,” having Ser. No. 14 / 956,131, and filing date Dec. 1, 2015.BACKGROUND[0003]Field[0004]The disclosed embodiments relate to anomaly detection. More specifically, the disclosed embodiments relate to techniqu...

AI Technical Summary

Problems solved by technology

An anomaly or failure in a server or data center may disrupt access to the service or resources, potentially resulting in lost business for the company and / or a reduction in consumer confidence that results in a loss of future business.

For example, high latency in loading web pages from the company's website may negatively impact the user experience with the website and deter some users from returning to the website.

The distributed nature of web-based resources may complicate the accurate detection and analysis of web performance anomalies and failures.

At the same time, a custom threshold for each data center may be unable to account for fluctuations in network traffic.

However, a threshold that does not account for peaks and troughs in network traffic may generate many false alarms during the peaks and detect only large anomalies during the troughs.

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